OpenRaiser/Intern-Atlas huggingface dataset:
MORCoRA: Multi-Objective Refactoring Recommendation Considering Review Availability
Venue: arXiv cs.SE 2026
Year: 2026

Background: Search-based refactoring involves searching for a sequence of refactorings to achieve specific objectives. Although a typical objective is improving code quality, a different perspective is also required; the searched sequence must undergo review before being applied and may not be applied if the review fails or is postponed due to no proper reviewers. Aim: Therefore, it is essential to ensure that the searched sequence of refactorings can be reviewed promptly by reviewers who meet two criteria: 1) having enough expertise and 2) being free of heavy workload. The two criteria are regarded as the review availability of the refactoring sequence. Method: We propose MORCoRA, a multi-objective search-based technique that can search for code quality improvable, semantic preserved, and high review availability possessed refactoring sequences and corresponding proper reviewers. Results: We evaluate MORCoRA on six open-source repositories. The quantitative analysis reveals that MORCoRA can effectively recommend refactoring sequences that fit the requirements. The qualitative analysis demonstrates that the refactorings recommended by MORCoRA can enhance code quality and effectively address code smells. Furthermore, the recommended reviewers for those refactorings possess high expertise and are available to review. Conclusions: We recommend that refactoring recommenders consider both the impact on quality improvement and the developer resources required for review when recommending refactorings.
Sparse Variational Student-t Processes for Heavy-tailed Modeling
Venue: arXiv cs.LG 2026
Year: 2026

The Gaussian process (GP) is a powerful tool for nonparametric modeling, but its sensitivity to outliers limits its applicability to data distributions with heavy-tails. Studentt processes offer a robust alternative for heavy tail modeling, but they lack the scalable developments of the GP to large datasets necessary for practical applications. We present Sparse Variational Student-t Processes (SVTP), the first principled framework that extends the sparse inducing point method to the Student-t process. We develop two novel inference algorithms, SVTP-UB and SVTP-MC, with theoretical guarantees, and derive a natural gradient optimization that exploits a previously unused connection between the Fisher information matrix of the multivariate Student-t distribution and the beta function (the 'beta link'). Experiments on UCI and Kaggle datasets demonstrate that SVTP significantly outperforms sparse GPs on when the data is contains outliers and heavy tails, achieving up to 3 times faster convergence and 40% lower prediction error while maintaining computational efficiency for datasets with over 200,000 samples.
A 1.6-fJ/Spike Subthreshold Analog Spiking Neuron in 28 nm CMOS
Venue: arXiv cs.NE 2026
Year: 2026

The computational complexity of deep learning algorithms has given rise to significant speed and memory challenges for the execution hardware. In energy-limited portable devices, highly efficient processing platforms are indispensable for reproducing the prowess afforded by much bulkier processing platforms. In this work, we present a low-power Leaky Integrate-and-Fire (LIF) neuron design fabricated in TSMC's 28 nm CMOS technology as proof of concept to build an energy-efficient mixed-signal Neuromorphic System-on-Chip (NeuroSoC). The fabricated neuron consumes 1.61 fJ/spike and occupies an active area of 34 $μm^{2}$, leading to a maximum spiking frequency of 300 kHz at 250 mV power supply. These performances are used in a software model to emulate the dynamics of a Spiking Neural Network (SNN). Employing supervised backpropagation and a surrogate gradient technique, the resulting accuracy on the MNIST dataset, using 4-bit post-training quantization stands at 82.5\%. The approach underscores the potential of such ASIC implementation of quantized SNNs to deliver high-performance, energy-efficient solutions to various embedded machine-learning applications.
Image class translation: visual inspection of class-specific hypotheticals and classification based on translation distance
Venue: arXiv cs.CV 2026
Year: 2026

Purpose: A major barrier to the implementation of artificial intelligence for medical applications is the lack of explainability and high confidence for incorrect decisions, specifically with out-of-domain samples. We propose a generalization of image translation networks for image classification and demonstrate their potential as a more interpretable alternative to conventional black-box classifiers. Approach: We train an image2image network to translate an input image to class-specific hypotheticals, and then compare these with the input, both visually and quantitatively. Translation distances, i.e., the degree of alteration needed to conform to one class or another, are examined for clusters and trends, and used as simple low-dimensional feature vectors for classification. Results: On melanoma/benign dermoscopy images, a translation distance classifier achieved 80% accuracy using only a 2-dimensional feature space (versus 85% for a conventional CNN using a ~62,000-dimensional feature space). Visual inspection of rendered images revealed dataset biases, such as scalebars, vignetting, and pale background pigmentation in melanomas. Image distributions in translation distance space revealed a natural separation along the lines of dermatologist decision to biopsy, rather than between malignant and benign. On bone marrow cytology images, translation distance classifiers outperformed a conventional CNN in both 3-class (92% accuracy vs 89% for CNN) and 6-class (90% vs 86% for CNN) scenarios. Conclusions: This proof-of-concept shows the potential for image2image networks to go beyond artistic/stylistic changes and to expose dataset biases, perform dimension reduction and dataset visualization, and in some cases, potentially outperform conventional end-to-end CNN classifiers.
Practical Aspects on Solving Differential Equations Using Deep Learning: A Primer
Venue: arXiv cs.LG 2026
Year: 2026

Deep learning has become a popular tool across many scientific fields, including the study of differential equations, particularly partial differential equations. This work introduces the basic principles of deep learning and the Deep Galerkin method, which uses deep neural networks to solve differential equations. This primer aims to provide technical and practical insights into the Deep Galerkin method and its implementation. We demonstrate how to solve the one-dimensional heat equation step-by-step. We also show how to apply the Deep Galerkin method to solve systems of ordinary differential equations and integral equations, such as the Fredholm of the second kind. Additionally, we provide code snippets within the text and the complete source code on Github. The examples are designed so that one can run them on a simple computer without needing a GPU.
Effective Wordle Heuristics
Venue: arXiv cs.IT 2026
Year: 2026

While previous researchers have performed an exhaustive search to determine an optimal Wordle strategy, that computation is very time consuming and produced a strategy using words that are unfamiliar to most people. With Wordle solutions being gradually eliminated (with a new puzzle each day and no reuse), an improved strategy could be generated each day, but the computation time makes a daily exhaustive search impractical. This paper shows that simple heuristics allow for fast generation of effective strategies and that little is lost by guessing only words that are possible solution words rather than more obscure words.
Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack Segmentation
Venue: arXiv cs.CV 2026
Year: 2026

Accurately segmenting structural cracks at the pixel level remains a major hurdle, as existing methods fail to integrate local textures with pixel dependencies, often leading to fragmented and incomplete predictions. Moreover, their high parameter counts and substantial computational demands hinder practical deployment on resource-constrained edge devices. To address these challenges, we propose CrackSCF, a Lightweight Cascaded Fusion Crack Segmentation Network designed to achieve robust crack segmentation with exceptional computational efficiency. We design a lightweight convolutional block (LRDS) to replace all standard convolutions. This approach efficiently captures local patterns while operating with a minimal computational footprint. For a holistic perception of crack structures, a lightweight Long-range Dependency Extractor (LDE) captures global dependencies. These are then intelligently unified with local patterns by our Staircase Cascaded Fusion Module (SCFM), ensuring the final segmentation maps are both seamless in continuity and rich in fine-grained detail. To comprehensively evaluate our method, this paper created the challenging TUT benchmark dataset and evaluated it alongside five other public datasets. The experimental results show that the CrackSCF method consistently outperforms the existing methods, and it demonstrates greater robustness in dealing with complex background noise. On the TUT dataset, CrackSCF achieved 0.8382 on F1 score and 0.8473 on mIoU, and it only required 4.79M parameters.
MCTR: Multi Camera Tracking Transformer
Venue: arXiv cs.CV 2026
Year: 2026

Multi-camera tracking plays a pivotal role in various real-world applications. While end-to-end methods have gained significant interest in single-camera tracking, multi-camera tracking remains predominantly reliant on heuristic techniques. In response to this gap, this paper introduces Multi-Camera Tracking tRansformer (MCTR), a novel end-to-end approach tailored for multi-object detection and tracking across multiple cameras with overlapping fields of view. MCTR leverages end-to-end detectors like DEtector TRansformer (DETR) to produce detections and detection embeddings independently for each camera view. The framework maintains set of track embeddings that encaplusate global information about the tracked objects, and updates them at every frame by integrating the local information from the view-specific detection embeddings. The track embeddings are probabilistically associated with detections in every camera view and frame to generate consistent object tracks. The soft probabilistic association facilitates the design of differentiable losses that enable end-to-end training of the entire system. To validate our approach, we conduct experiments on MMPTrack and AI City Challenge, two recently introduced large-scale multi-camera multi-object tracking datasets.
BOP-Distrib: Revisiting 6D Pose Estimation Benchmarks for Better Evaluation under Visual Ambiguities
Venue: arXiv cs.CV 2026
Year: 2026

6D pose estimation aims at determining the object pose that best explains the camera observation. The unique solution for non-ambiguous objects can turn into a multi-modal pose distribution for symmetrical objects or when occlusions of symmetry-breaking elements happen, depending on the viewpoint. Currently, 6D pose estimation methods are benchmarked on datasets that consider, for their ground truth annotations, visual ambiguities as only related to global object symmetries, whereas they should be defined per-image to account for the camera viewpoint. We thus first propose an automatic method to re-annotate those datasets with a 6D pose distribution specific to each image, taking into account the object surface visibility in the image to correctly determine the visual ambiguities. Second, given this improved ground truth, we re-evaluate the state-of-the-art single pose methods and show that this greatly modifies the ranking of these methods. Third, as some recent works focus on estimating the complete set of solutions, we derive a precision/recall formulation to evaluate them against our image-wise distribution ground truth, making it the first benchmark for pose distribution methods on real images.
On Expressive Power of Quantized Neural Networks under Fixed-Point Arithmetic
Venue: arXiv cs.LG 2026
Year: 2026

Existing works on the expressive power of neural networks typically assume real parameters and exact operations. In this work, we study the expressive power of quantized networks under discrete fixed-point parameters and inexact fixed-point operations with round-off errors. We first provide a necessary condition and a sufficient condition on fixed-point arithmetic and activation functions for quantized networks to represent all fixed-point functions from fixed-point vectors to fixed-point numbers. Then, we show that various popular activation functions satisfy our sufficient condition, e.g., Sigmoid, ReLU, ELU, SoftPlus, SiLU, Mish, and GELU. In other words, networks using those activation functions are capable of representing all fixed-point functions. We further show that our necessary condition and sufficient condition coincide under a mild condition on activation functions: e.g., for an activation function $σ$, there exists a fixed-point number $x$ such that $σ(x)=0$. Namely, we find a necessary and sufficient condition for a large class of activation functions. We lastly show that even quantized networks using binary weights in $\{-1,1\}$ can also represent all fixed-point functions for practical activation functions.
SoK: Security of the Image Processing Pipeline for Camera-based Sensing in Autonomous Vehicles
Venue: arXiv cs.CR 2026
Year: 2026

Cameras capture images that are essential for many safety-critical tasks. To process these images, a complex pipeline with multiple layers is used. Security attacks on this pipeline can severely affect passenger safety and system performance. However, many attacks presented in scientific literature overlook the fact that there are different layers and, hence, the feasibility and impact of these attacks can vary. While there has been research to improve the quality and robustness of the image processing pipeline, these efforts are often orthogonal to security research without exploiting potential overlap and synergies. In this work, we aim to bridge this gap by combining security and robustness research for the image processing pipeline in autonomous vehicles. We thoroughly investigated the body of literature on the security and robustness of the image processing pipeline and selected 92 papers for deeper discussion in this SoK. For the security domain, we classify the risk of attacks using the automotive security standard ISO 21434, emphasizing the need to consider all layers for overall system security. With our online tool TARA-CAM, we propose an interactive method to perform threat analysis and risk assessment following the ISO standard. We also demonstrate how existing robustness research can help mitigate the impact of attacks, addressing the current research gap. Finally, we present PICT, an embedded open-source testbed that can influence various parameters across all layers, allowing researchers to analyze the effects of different defense strategies and attack impacts. With this SoK, we contribute a comprehensive discussion and systematic analysis of existing approaches to image processing pipeline security and robustness, together with an open-source tool and testbed that jointly facilitates hardening the image processing pipeline against existing and future security attacks.
Multiview Random Vector Functional Link Network for Predicting DNA-Binding Proteins
Venue: arXiv cs.LG 2026
Year: 2026

The identification of DNA-binding proteins (DBPs) is essential due to their significant impact on various biological activities. Understanding the mechanisms underlying protein-DNA interactions is essential for elucidating various life activities. In recent years, machine learning-based models have been prominently utilized for DBP prediction. In this paper, to predict DBPs, we propose a novel framework termed a multiview random vector functional link (MvRVFL) network, which fuses neural network architecture with multiview learning. The MvRVFL model integrates both late and early fusion advantages, enabling separate regularization parameters for each view, while utilizing a closed-form solution for efficiently determining unknown parameters. The primal objective function incorporates a coupling term aimed at minimizing a composite of errors stemming from all views. From each of the three protein views of the DBP datasets, we extract five features. These features are then fused together by incorporating a hidden feature during the model training process. The performance of the proposed MvRVFL model on the DBP dataset surpasses that of baseline models, demonstrating its superior effectiveness. We further validate the practicality of the proposed model across diverse benchmark datasets, and both theoretical analysis and empirical results consistently demonstrate its superior generalization performance over baseline models.
An invariant-theoretic approach to three weight enumerators of self-dual quantum codes
Venue: arXiv cs.IT 2026
Year: 2026

This article is a continuation of our recent work (Yin Chen and Runxuan Zhang, Shape enumerators of self-dual NRT codes over finite fields. SIAM J. Discrete Math. 38 (2024), no. 4, 2841-2854) in the setting of quantum error-correcting codes. We use algebraic invariant theory to study three weight enumerators of formally self-dual quantum codes over arbitrary finite fields. We derive a quantum analogue of Gleason's theorem, demonstrating that the weight enumerator of a formally self-dual quantum code can be expressed algebraically by two polynomials. We also show that the double weight enumerator of a formally self-dual quantum code can be expressed algebraically by five polynomials. We explicitly compute the complete weight enumerators of some special self-dual quantum codes. Our approach illustrates the potential of employing algebraic invariant theory to compute weight enumerators of self-dual quantum codes.
Multimodal Laryngoscopic Video Analysis for Assisted Diagnosis of Vocal Fold Paralysis
Venue: arXiv cs.SD 2026
Year: 2026

This paper presents the Multimodal Laryngoscopic Video Analyzing System (MLVAS), a novel system that leverages both audio and video data to automatically extract key video segments and metrics from raw laryngeal videostroboscopic videos for assisted clinical assessment. The system integrates video-based glottis detection with an audio keyword spotting method to analyze both video and audio data, identifying patient vocalizations and refining video highlights to ensure optimal inspection of vocal fold movements. Beyond key video segment extraction from the raw laryngeal videos, MLVAS is able to generate effective audio and visual features for Vocal Fold Paralysis (VFP) detection. Pre-trained audio encoders are utilized to encode the patient voice to get the audio features. Visual features are generated by measuring the angle deviation of both the left and right vocal folds to the estimated glottal midline on the segmented glottis masks. To get better masks, we introduce a diffusion-based refinement that follows traditional U-Net segmentation to reduce false positives. We conducted several ablation studies to demonstrate the effectiveness of each module and modalities in the proposed MLVAS. The experimental results on a public segmentation dataset show the effectiveness of our proposed segmentation module. In addition, unilateral VFP classification results on a real-world clinic dataset demonstrate MLVAS's ability of providing reliable and objective metrics as well as visualization for assisted clinical diagnosis.
Random-Restart Best-Response Dynamics for Large-Scale Integer Programming Games and Their Applications
Venue: arXiv cs.GT 2026
Year: 2026

This paper presents scalable algorithms for computing pure Nash equilibria (PNEs) in large-scale integer programming games (IPGs), where existing exact methods typically handle only small numbers of players. Motivated by a county-level aquatic invasive species (AIS) prevention problem with 84 decision makers, we develop and analyze random-restart best-response dynamics (RR-BRD), a randomized search framework for PNEs. For IPGs with finite action sets, we model RR-BRD as a Markov chain on the best-response state graph and show that, whenever a PNE exists and the restart law has positive probability of reaching a PNE within the round cap, RR-BRD finds a PNE almost surely. We also propose a Monte Carlo sampling-and-simulation procedure to estimate success behavior under a fixed round cap, which informs our instance-dependent performance characterization. We then embed RR-BRD as a randomized local-search subroutine within the zero-regret (ZR) framework, yielding BRD-incorporated zero-regret (BZR). Using solver callbacks, RR-BRD searches for and supplies PNEs, while ZR separates and adds equilibrium inequalities to tighten the formulation. We introduce edge-weighted budgeted maximum coverage (EBMC) games to model AIS prevention and establish PNE existence results for both selfish and locally altruistic utilities. Computational experiments on synthetic EBMC and knapsack problem game instances show that RR-BRD and BZR scale equilibrium computation up to $n \le 30$ players. We further solve a real-world EBMC game derived from the Minnesota AIS dataset with $n = 84$ county players.
Medal Matters: Probing LLMs' Failure Cases Through Olympic Rankings
Venue: arXiv cs.CL 2026
Year: 2026

Large language models (LLMs) have achieved remarkable success in natural language processing tasks, yet their internal knowledge structures remain poorly understood. This study examines these structures through the lens of historical Olympic medal tallies, evaluating LLMs on two tasks: (1) retrieving medal counts for specific teams and (2) identifying rankings of each team. While state-of-the-art LLMs excel in recalling medal counts, they struggle with providing rankings, highlighting a key difference between their knowledge organization and human reasoning. These findings shed light on the limitations of LLMs' internal knowledge integration and suggest directions for improvement. To facilitate further research, we release our code, dataset, and model outputs.
Semantic2D: Enabling Semantic Scene Understanding with 2D Lidar Alone
Venue: arXiv cs.RO 2026
Year: 2026

This article presents a complete semantic scene understanding workflow using only a single 2D lidar. This fills the gap in 2D lidar semantic segmentation, thereby enabling the rethinking and enhancement of existing 2D lidar-based algorithms for application in various mobile robot tasks. It introduces the first publicly available 2D lidar semantic segmentation dataset and the first fine-grained semantic segmentation algorithm specifically designed for 2D lidar sensors on autonomous mobile robots. To annotate this dataset, we propose a novel semi-automatic semantic labeling framework that requires minimal human effort and provides point-level semantic annotations. The data was collected by three different types of 2D lidar sensors across twelve indoor environments, featuring a range of common indoor objects. Furthermore, the proposed semantic segmentation algorithm fully exploits raw lidar information -- position, range, intensity, and incident angle -- to deliver stochastic, point-wise semantic segmentation. We present a series of semantic occupancy grid mapping experiments and demonstrate two semantically-aware navigation control policies based on 2D lidar. These results demonstrate that the proposed semantic 2D lidar dataset, semi-automatic labeling framework, and segmentation algorithm are effective and can enhance different components of the robotic navigation pipeline. Multimedia resources are available at: https://youtu.be/P1Hsvj6WUSY.